Soft Computing

• 26 hrs lectures
• 26 hrs projects

• 55 pts final exam (written part)
• 15 pts mid-term test (written part)
• 30 pts projects

• Students will acquaint with basic types of neural networks and with their applications.
• Students will acquaint with fundamentals of theory of fuzzy sets and fuzzy logic including design of fuzzy controller.
• Students will acquaint with nature-inspired optimization algorithms.
• Students will acquaint with fundamentals of probability reasoning theory.
• Students will acquaint with fundamentals of rouhg sets theory and with use of these sets for data mining.
• Students will acquaint with fundamentals of chaos theory.

• Students will learn terminology in Soft-computing field both in Czech and in English languages.
• Students awake the importance of tolerance of imprecision and uncertainty for design of robust and low-cost intelligent machines.

To give students knowledge of soft-computing theories fundamentals, i.e. of fundamentals of non-traditional technologies and approaches to solving hard real-world problems.

By studying the subject, students will gain knowledge of working with vague, uncertain and incomplete information that is essential for successful intelligent system designs.

• Programming in C++ or Java languages.
• Basic knowledge of differential calculus and probability theory.

• Graube, D.: Principles of Artificial Neural networks, World Scientific Publishing Co. Pte. Ltd., third edition, 2013
• Kriesel, D.: A Brief Introduction to Neural Networks, 2005, http://www.dkriesel.com/en/science/neural_networks
• Kruse, R., Borgelt, Ch., Braune, Ch., Mostaghim, S., Steinbrecher, M.: Computational Intelligence, Springer, second edition 2016, ISBN 978-1-4471-7296-3
• Munakata, T.: Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008, ISBN 978-1-84628-838-8
• Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
• Russell, S., Norvig, P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
• Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7

• Kriesel, D.: A Brief Introduction to Neural Networks, 2005, Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8 
• Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
• Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7

1. Introduction. Biological and artificial neuron, artificial neural networks.
2. Acyclic and feedforward neural networks, backpropagation algorithm. 
3. Neural networks with RBF neurons. Competitive networks.
4. Neocognitron and convolutional neural networks.
5. Recurrent neural networks (Hopfield networks, Boltzmann machine).
6. Recurrent neural networks (LSTM, GRU).
7. Genetic algorithms.
8. Optimization algorithms inspired by nature.
9. Fuzzy sets and fuzzy logic.
10. Probabilistic reasoning, Bayesian networks.
11. Rough sets.
12. Chaos.
13. Hybrid approaches (neural networks, fuzzy logic, genetic algorithms).

Individual project - solving real-world problem (classification, optimization, association, controlling).

• Mid-term written examination - 15 points.
• Project - 30 points.
• Final written examination - 55 points; The minimal number of points necessary for successful clasification is 25 (otherwise, no points will be assigned).

Exam prerequisites:
At least 20 points earned during semester (mid-term test and project).

At least 20 points earned during semester (mid-term test and project).